Crowdfunding and sustainable development: A systematic review
Bibliographic record
Abstract
• This paper offers a bibliometric review of 148 articles on crowdfunding and SDGs. • Using performance analysis and science mapping, we identify four research clusters. • This study contributes to the literature on sustainability-oriented crowdfunding. • It offers practical implications and a detailed roadmap for future research. This study employs bibliometric analysis to explore the evolving role of crowdfunding in financing sustainable development goals (SDGs). Analyzing 148 peer-reviewed articles (2014–2024), it identifies key trends, influential contributors, and thematic clusters in academic discourse. Findings reveal a surge in research post-2020, with a focus on entrepreneurial finance, environmental sustainability, and financial innovation. Equity crowdfunding and FinTech emerge as pivotal in bridging sustainability-related funding gaps. Cluster analysis highlights four major research areas: financial innovation's role in sustainability, crowdfunding's contribution to SDGs (especially post-COVID-19), microfinancing and financial inclusion for SMEs, and ESG integration in entrepreneurial finance. Despite these advances, significant research gaps remain, particularly the need for longitudinal studies to assess the long-term impacts of crowdfunding on sustainability, as well as a deeper understanding of the ethical implications surrounding governance and backer protection on crowdfunding platforms. This study contributes to the growing body of literature on sustainability-oriented crowdfunding by offering a detailed roadmap for future research and practical implications for scholars and practitioners alike.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".